Self-loops in evolutionary graph theory: Friends or foes?
Fig 5
Sampling mutant’s fitness from the Gaussian.
(A) When mutant fitness is sampled from the (truncated) Gaussian distribution with σ = 0.1, we find that adding self-loops decreases the population fitness in all the graphs. (B) Increasing the σ from 0.1 to 1, the average fitness in the steady-state goes down for many graphs. The effect of increasing the σ is largest in the heterogeneous star graphs and smallest in the more homogeneous structure like the complete graph. (C) We recover the uniform mutant fitness distribution case for very large σ, here σ = 10. In this case, all the non-self looped graphs attain the same steady-state. All self-looped graphs have lower average steady-state fitness than a non-self looped graph and the self-looped complete graph (Parameters: N = 10, μ = 1, fmin = 0.1, fmax = 10, 2000 independent realisations).